{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node representation learning with Deep Graph Infomax\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/deep-graph-infomax-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/deep-graph-infomax-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo demonstrates how to perform unsupervised training of several models using [the Deep Graph Infomax algorithm](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.DeepGraphInfomax) (https://arxiv.org/pdf/1809.10341.pdf) on the CORA dataset:\n",
    "\n",
    "- [GCN](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GCN) (both as a full-batch method, and with [the Cluster-GCN training procedure](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.mapper.ClusterNodeGenerator))\n",
    "- [GAT](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GAT) (only as a full-batch method, but the Cluster-GCN training procedure is also supported)\n",
    "- [APPNP](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.APPNP) (as with GAT, only as a full-batch method, but the Cluster-GCN training procedure is also supported)\n",
    "- [RGCN](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.RGCN)\n",
    "- [GraphSAGE](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GraphSAGE)\n",
    "- [HinSAGE](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.HinSAGE)\n",
    "\n",
    "As with all StellarGraph workflows: first we load the dataset, next we create our data generators, and then we train our model. We then take the embeddings created through unsupervised training and predict the node classes using logistic regression.\n",
    "\n",
    "> See [the GCN + Deep Graph Infomax fine-tuning demo](../node-classification/gcn-deep-graph-infomax-fine-tuning-node-classification.ipynb) for semi-supervised training using Deep Graph Infomax, by fine-tuning the base model for node classification using labelled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.mapper import (\n",
    "    CorruptedGenerator,\n",
    "    FullBatchNodeGenerator,\n",
    "    GraphSAGENodeGenerator,\n",
    "    HinSAGENodeGenerator,\n",
    "    ClusterNodeGenerator,\n",
    ")\n",
    "from stellargraph import StellarGraph\n",
    "from stellargraph.layer import GCN, DeepGraphInfomax, GraphSAGE, GAT, APPNP, HinSAGE\n",
    "\n",
    "from stellargraph import datasets\n",
    "from stellargraph.utils import plot_history\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.manifold import TSNE\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, node_subjects = dataset.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generators\n",
    "\n",
    "Now we create the data generators using `CorruptedGenerator` ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.mapper.CorruptedGenerator)). `CorruptedGenerator` returns shuffled node features along with the regular node features and we train our model to discriminate between the two. \n",
    "\n",
    "Note that:\n",
    "\n",
    "- We typically pass all nodes to `corrupted_generator.flow` because this is an unsupervised task\n",
    "- We don't pass `targets` to `corrupted_generator.flow` because these are binary labels (true nodes, false nodes) that are created by `CorruptedGenerator`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "fullbatch_generator = FullBatchNodeGenerator(G, sparse=False)\n",
    "gcn_model = GCN(layer_sizes=[128], activations=[\"relu\"], generator=fullbatch_generator)\n",
    "\n",
    "corrupted_generator = CorruptedGenerator(fullbatch_generator)\n",
    "gen = corrupted_generator.flow(G.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GCN Model Creation and Training\n",
    "\n",
    "We create and train our `DeepGraphInfomax` model ([docs](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.DeepGraphInfomax)). Note that the loss used here must always be `tf.nn.sigmoid_cross_entropy_with_logits`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "infomax = DeepGraphInfomax(gcn_model, corrupted_generator)\n",
    "x_in, x_out = infomax.in_out_tensors()\n",
    "\n",
    "model = Model(inputs=x_in, outputs=x_out)\n",
    "model.compile(loss=tf.nn.sigmoid_cross_entropy_with_logits, optimizer=Adam(lr=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "es = EarlyStopping(monitor=\"loss\", min_delta=0, patience=20)\n",
    "history = model.fit(gen, epochs=epochs, verbose=0, callbacks=[es])\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Embeddings and Logistic Regression\n",
    "\n",
    "Since we've already trained the weights of our base model - GCN in this example - we can simply use `base_model.in_out_tensors` to obtain the trained node embedding model. Then we use logistic regression on the node embeddings to predict which class the node belongs to.\n",
    "\n",
    "Note that the results here differ from the paper due to different train/test/val splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_emb_in, x_emb_out = gcn_model.in_out_tensors()\n",
    "\n",
    "# for full batch models, squeeze out the batch dim (which is 1)\n",
    "x_out = tf.squeeze(x_emb_out, axis=0)\n",
    "emb_model = Model(inputs=x_emb_in, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.8002461033634126\n"
     ]
    }
   ],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    node_subjects, train_size=0.1, test_size=None, stratify=node_subjects\n",
    ")\n",
    "\n",
    "test_gen = fullbatch_generator.flow(test_subjects.index)\n",
    "train_gen = fullbatch_generator.flow(train_subjects.index)\n",
    "\n",
    "test_embeddings = emb_model.predict(test_gen)\n",
    "train_embeddings = emb_model.predict(train_gen)\n",
    "\n",
    "lr = LogisticRegression(multi_class=\"auto\", solver=\"lbfgs\")\n",
    "lr.fit(train_embeddings, train_subjects)\n",
    "\n",
    "y_pred = lr.predict(test_embeddings)\n",
    "gcn_acc = (y_pred == test_subjects).mean()\n",
    "print(f\"Test classification accuracy: {gcn_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This accuracy is close to that for [training a supervised GCN model end-to-end](../node-classification/gcn-node-classification.ipynb), suggesting that Deep Graph Infomax is an effective method for unsupervised training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation with TSNE\n",
    "\n",
    "Here we visualize the node embeddings with TSNE. As you can see below, the Deep Graph Infomax model produces well separated embeddings using unsupervised training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_embeddings = emb_model.predict(fullbatch_generator.flow(G.nodes()))\n",
    "\n",
    "y = node_subjects.astype(\"category\")\n",
    "trans = TSNE(n_components=2)\n",
    "emb_transformed = pd.DataFrame(trans.fit_transform(all_embeddings), index=G.nodes())\n",
    "emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].cat.codes,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\"TSNE visualization of GCN embeddings for cora dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Different Models\n",
    "\n",
    "Now we run Deep Graph Infomax training for other models. Note that switching between StellarGraph models only requires a few code changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_deep_graph_infomax(\n",
    "    base_model, generator, epochs, reorder=lambda sequence, subjects: subjects\n",
    "):\n",
    "    corrupted_generator = CorruptedGenerator(generator)\n",
    "    gen = corrupted_generator.flow(G.nodes())\n",
    "    infomax = DeepGraphInfomax(base_model, corrupted_generator)\n",
    "\n",
    "    x_in, x_out = infomax.in_out_tensors()\n",
    "\n",
    "    model = Model(inputs=x_in, outputs=x_out)\n",
    "    model.compile(loss=tf.nn.sigmoid_cross_entropy_with_logits, optimizer=Adam(lr=1e-3))\n",
    "    history = model.fit(gen, epochs=epochs, verbose=0, callbacks=[es])\n",
    "\n",
    "    x_emb_in, x_emb_out = base_model.in_out_tensors()\n",
    "    # for full batch models, squeeze out the batch dim (which is 1)\n",
    "    if generator.num_batch_dims() == 2:\n",
    "        x_emb_out = tf.squeeze(x_emb_out, axis=0)\n",
    "\n",
    "    emb_model = Model(inputs=x_emb_in, outputs=x_emb_out)\n",
    "\n",
    "    test_gen = generator.flow(test_subjects.index)\n",
    "    train_gen = generator.flow(train_subjects.index)\n",
    "\n",
    "    test_embeddings = emb_model.predict(test_gen)\n",
    "    train_embeddings = emb_model.predict(train_gen)\n",
    "\n",
    "    # some generators yield predictions in a different order to the .flow argument,\n",
    "    # so we need to get everything lined up correctly\n",
    "    ordered_test_subjects = reorder(test_gen, test_subjects)\n",
    "    ordered_train_subjects = reorder(train_gen, train_subjects)\n",
    "\n",
    "    lr = LogisticRegression(multi_class=\"auto\", solver=\"lbfgs\")\n",
    "    lr.fit(train_embeddings, ordered_train_subjects)\n",
    "\n",
    "    y_pred = lr.predict(test_embeddings)\n",
    "    acc = (y_pred == ordered_test_subjects).mean()\n",
    "\n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster-GCN\n",
    "\n",
    "[Cluster-GCN](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.mapper.ClusterNodeGenerator) is a scalable training procedure for that works for several \"full batch\" models in StellarGraph, including GCN, GAT and APPNP. This example just trains on GCN. The training mechanism breaks the graph into a number of small subgraph \"clusters\" and trains a single GCN model on these, successively. It is equivalent to full-batch GCN with a single cluster (`clusters=1`), but with `clusters > 1` random clusters (as used here), its performance will be less than GCN. With better clusters, Cluster-GCN performance should be much improved.\n",
    "\n",
    "(Note: `ClusterNodeGenerator` can be [used with Neo4j](https://stellargraph.readthedocs.io/en/stable/demos/connector/neo4j/cluster-gcn-on-cora-neo4j-example.html) for scalable training on large graphs, including unsupervised via Deep Graph Infomax.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters 12\n",
      "0 cluster has size 225\n",
      "1 cluster has size 225\n",
      "2 cluster has size 225\n",
      "3 cluster has size 225\n",
      "4 cluster has size 225\n",
      "5 cluster has size 225\n",
      "6 cluster has size 225\n",
      "7 cluster has size 225\n",
      "8 cluster has size 225\n",
      "9 cluster has size 225\n",
      "10 cluster has size 225\n",
      "11 cluster has size 233\n",
      "Test classification accuracy: 0.6308449548810501\n"
     ]
    }
   ],
   "source": [
    "cluster_generator = ClusterNodeGenerator(G, clusters=12, q=4)\n",
    "cluster_gcn_model = GCN(\n",
    "    layer_sizes=[128], activations=[\"relu\"], generator=cluster_generator\n",
    ")\n",
    "\n",
    "\n",
    "def cluster_reorder(sequence, subjects):\n",
    "    # shuffle the subjects into the same order as the sequence yield\n",
    "    return subjects[sequence.node_order]\n",
    "\n",
    "\n",
    "cluster_gcn_acc = run_deep_graph_infomax(\n",
    "    cluster_gcn_model, cluster_generator, epochs=epochs, reorder=cluster_reorder\n",
    ")\n",
    "print(f\"Test classification accuracy: {cluster_gcn_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAT\n",
    "\n",
    "[GAT](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GAT) is a \"full batch\" model similar to GCN. It can also be trained using both `FullBatchNodeGenerator` and `ClusterNodeGenerator`, including for Deep Graph Infomax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.4716981132075472\n"
     ]
    }
   ],
   "source": [
    "gat_model = GAT(\n",
    "    layer_sizes=[128], activations=[\"relu\"], generator=fullbatch_generator, attn_heads=8,\n",
    ")\n",
    "gat_acc = run_deep_graph_infomax(gat_model, fullbatch_generator, epochs=epochs)\n",
    "\n",
    "gat_acc\n",
    "print(f\"Test classification accuracy: {gat_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPNP\n",
    "\n",
    "[APPNP](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.APPNP) is a \"full batch\" model similar to GCN. It can also be trained using both `FullBatchNodeGenerator` and `ClusterNodeGenerator`, including for Deep Graph Infomax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.440935192780968\n"
     ]
    }
   ],
   "source": [
    "appnp_model = APPNP(\n",
    "    layer_sizes=[128], activations=[\"relu\"], generator=fullbatch_generator\n",
    ")\n",
    "appnp_acc = run_deep_graph_infomax(appnp_model, fullbatch_generator, epochs=epochs)\n",
    "\n",
    "print(f\"Test classification accuracy: {appnp_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GraphSAGE\n",
    "\n",
    "[GraphSAGE](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.GraphSAGE) is a sampling model, different to the models above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.7210828547990156\n"
     ]
    }
   ],
   "source": [
    "graphsage_generator = GraphSAGENodeGenerator(G, batch_size=1000, num_samples=[5])\n",
    "\n",
    "graphsage_model = GraphSAGE(\n",
    "    layer_sizes=[128], activations=[\"relu\"], generator=graphsage_generator\n",
    ")\n",
    "graphsage_acc = run_deep_graph_infomax(\n",
    "    graphsage_model, graphsage_generator, epochs=epochs\n",
    ")\n",
    "\n",
    "print(f\"Test classification accuracy: {graphsage_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heterogeneous models\n",
    "\n",
    "Cora is a homogeneous graph, with only one type of node (`paper`) and one type of edge (`type`). Models designed for heterogeneous graphs (with more than one of either) can also be applied to homogeneous graphs, but it is not using their additional flexibility.\n",
    "\n",
    "#### HinSAGE\n",
    "\n",
    "[HinSAGE](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.HinSAGE) is a generalisation of GraphSAGE to heterogeneous graphs that can be trained with Deep Graph Infomax. For homogeneous graphs, it is equivalent to GraphSAGE and it indeed gives similar results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.7042657916324856\n"
     ]
    }
   ],
   "source": [
    "hinsage_generator = HinSAGENodeGenerator(\n",
    "    G, batch_size=1000, num_samples=[5], head_node_type=\"paper\"\n",
    ")\n",
    "\n",
    "hinsage_model = HinSAGE(\n",
    "    layer_sizes=[128], activations=[\"relu\"], generator=hinsage_generator\n",
    ")\n",
    "hinsage_acc = run_deep_graph_infomax(hinsage_model, hinsage_generator, epochs=epochs)\n",
    "\n",
    "print(f\"Test classification accuracy: {hinsage_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RGCN\n",
    "\n",
    "[RGCN](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.RGCN) is a generalisation of GCN to heterogeneous graphs (with multiple edge types) that can be trained with Deep Graph Infomax. For homogeneous graphs, it is similar to GCN. It normalises the graph's adjacency matrix in a different manner and so won't exactly match it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test classification accuracy: 0.7366694011484823\n"
     ]
    }
   ],
   "source": [
    "from stellargraph.mapper import RelationalFullBatchNodeGenerator\n",
    "from stellargraph.layer import RGCN\n",
    "\n",
    "rgcn_generator = RelationalFullBatchNodeGenerator(G)\n",
    "\n",
    "rgcn_model = RGCN(layer_sizes=[128], activations=[\"relu\"], generator=rgcn_generator)\n",
    "\n",
    "rgcn_acc = run_deep_graph_infomax(rgcn_model, rgcn_generator, epochs=epochs)\n",
    "print(f\"Test classification accuracy: {rgcn_acc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall results\n",
    "\n",
    "The cell below shows the accuracy of each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>GAT</td>\n",
       "      <td>0.471698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GCN</td>\n",
       "      <td>0.800246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cluster-GCN</td>\n",
       "      <td>0.630845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>APPNP</td>\n",
       "      <td>0.440935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GraphSAGE</td>\n",
       "      <td>0.721083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HinSAGE</td>\n",
       "      <td>0.704266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RGCN</td>\n",
       "      <td>0.736669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Accuracy\n",
       "GAT          0.471698\n",
       "GCN          0.800246\n",
       "Cluster-GCN  0.630845\n",
       "APPNP        0.440935\n",
       "GraphSAGE    0.721083\n",
       "HinSAGE      0.704266\n",
       "RGCN         0.736669"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    [gat_acc, gcn_acc, cluster_gcn_acc, appnp_acc, graphsage_acc, hinsage_acc, rgcn_acc],\n",
    "    index=[\"GAT\", \"GCN\", \"Cluster-GCN\", \"APPNP\", \"GraphSAGE\", \"HinSAGE\", \"RGCN\"],\n",
    "    columns=[\"Accuracy\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook demonstrated how to use [the Deep Graph Infomax algorithm](https://stellargraph.readthedocs.io/en/stable/api.html#stellargraph.layer.DeepGraphInfomax) to train other algorithms to yield useful embedding vectors for nodes, without supervision. To validate the quality of these vectors, it used logistic regression to perform a supervised node classification task.\n",
    "\n",
    "See [the GCN + Deep Graph Infomax fine-tuning demo](../node-classification/gcn-deep-graph-infomax-fine-tuning-node-classification.ipynb) for semi-supervised training using Deep Graph Infomax, by fine-tuning the base model for node classification using labelled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/deep-graph-infomax-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/deep-graph-infomax-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
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